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Distance Perception in Real and Virtual Environments

JODIE M. PLUMERT, JOSEPH K. KEARNEY, JAMES F. CREMER, and KARA RECKERThe University of Iowa

We conducted three experiments to compare distance perception in real and virtual environments. In Experiment 1, adultsestimated how long it would take to walk to targets in real and virtual environments by starting and stopping a stopwatch whilelooking at a target person standing between 20 and 120 ft away. The real environment was a large grassy lawn in front of auniversity building. We replicated this scene in our virtual environment using a nonstereoscopic, large-screen immersive displaysystem. We found that people underestimated time to walk in both environments for distances of 40 to 60 ft and beyond. However,time-to-walk estimates were virtually identical across the two environments, particularly when people made real environmentestimates first. In Experiment 2, 10- and 12-year-old children and adults estimated time to walk in real and virtual environmentsboth with and without vision. Adults underestimated time to walk in both environments for distances of 60 to 80 ft and beyond.Again, their estimates were virtually identical in the real and virtual environment both with and without vision. Twelve-year-olds’ time-to-walk estimates were also very similar across the two environments under both viewing conditions, but 10-year-oldsexhibited greater underestimation in the virtual than in the real environment. A third experiment showed that adults’ time-to-walk estimates were virtually identical to walking without vision. We conclude that distance perception may be better in virtualenvironments involving large-screen immersive displays than in those involving head-mounted displays (HMDS).

Categories and Subject Descriptors: J.4 [Computer Applications]: Social and Behavioral Sciences—Psychology; I.3.7 [Com-puter Graphics]: Three Dimensional Graphics and Realism—Virtual reality

General Terms: Experimentation, Measurement, Performance

Additional Key Words and Phrases: Virtual environments, large-screen immersive displays, distance estimation, perception

1. INTRODUCTION

Virtual environments are gaining widespread acceptance as a tool for studying human behavior [Loomiset al. 1999; Plumert et al. 2004]. Problems ranging from children’s road-crossing behavior (Plumertet al. 2004) to adults’ collision-avoidance behavior [Cutting et al. 1995] have been studied using variouskinds of virtual environments. One obvious question that arises when using virtual environments tostudy human behavior is how well does behavior in virtual environments correspond to behavior inthe real environment? Although virtual environments are an exciting new medium for investigatingdifficult-to-study problems under realistic and controlled conditions, the results of such experimentsare of questionable value if virtual environments lack ecological validity. One critical aspect of behaviorin virtual environments that has received increasing attention is distance perception. Clearly, distanceperception underlies a wide range of human action in both natural and virtual environments. Tasks suchas throwing a ball at a target or steering a bike around an obstacle require that observers accurately

Authors’ addresses: Jodie Plumert and Kara Recker, Department of Psychology, The University of Iowa, 52242; email: [email protected], [email protected]; Joseph Kearney and James Cremer, Department of Computer Science, The Univer-sity of Iowa, Iowa City, IA 52242; email: [email protected],[email protected] to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee providedthat copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the firstpage or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists,or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requestedfrom Publications Dept., ACM, Inc., 1515 Broadway, New York, NY 10036 USA, fax: +1 (212) 869-0481, or [email protected]© 2005 ACM 1544-3558/05/0700-0216 $5.00

ACM Transactions on Applied Perception, Vol. 2, No. 3, July 2005, Pages 216–233.

Distance Perception in Real and Virtual Environments • 217

perceive how far away things are from themselves. Given the importance of distance perception forhuman action, it is critical to assess how well distance perception corresponds in real and virtualenvironments. We addressed this question by asking people to judge the same distances both in thereal environment and in an immersive virtual environment.

How good are people at perceiving distance in the natural environment? A relatively large number ofstudies have been conducted on how people perceive egocentric distance (i.e., absolute distance from self)and exocentric distance (i.e., relative distance between objects) in the natural environment. Studies thatuse visually guided judgments to assess perception of distance typically find that people progressivelyundershoot egocentric distance with increasing physical distance [e.g., Gilinsky 1951; Harway 1963;Loomis et al. 1992]. For example, when asked to match a depth interval on the ground plane withan interval in the frontal plane, people consistently chose depth intervals that were too large [Loomiset al. 1992]. In other words, people perceived the same interval as shorter in the depth plane than inthe frontal plane. In contrast, studies that use visually directed action to assess perception of distancetypically find that people are very good at perceiving distances [Loomis et al. 1992; Philbeck and Loomis1997; Rieser et al. 1990]. These studies have shown that people are quite accurate at walking withoutvision to previously seen targets, particularly within what is called action space (up to about 20 m).Beyond 20 m or so, people tend to undershoot distances when walking without vision. Together, thesestudies suggest that the mapping between visual and physical space is distorted in perception, but notaction.

How good are people at perceiving distance in virtual environments? A number of recent studies sug-gest that people underestimate distance in virtual environments [Loomis and Knapp 2003; Thompsonet al. 2004; Willemsen and Gooch 2002]. Loomis and Knapp [2003], for example, examined percep-tion of egocentric distance in a virtual environment using a stereoscopic HMD system. People viewedspheres lying on the ground plane at distances of 2, 6, and 18 m. Using a triangulation task, people firstviewed the target, then turned from the target, and then walked without vision for about 3 m. At thestopping point, people attempted to point to the previously viewed target. Pointing errors showed thatpeople undershot distances by about a factor of 2. In contrast, other work has shown relatively littlecompression of distance in virtual environments [Interrante et al. 2004; Witmer and Sadowski 1998].Witmer and Sadowski [1998] compared blindfolded walking in a real hallway to blindfolded walking ona treadmill in a virtual hallway. In both environments, people saw a target and then attempted to walkto it without vision. They found that mean errors varied between 1 and 11% of the target distances inthe real environment and between 2 and 18% in the virtual environment. Mean error increased linearlywith increasing target distance, but did not differ significantly across environments. In addition, peoplemade greater errors in both environments when they experienced the virtual environment first thanwhen they experienced the real environment first. A recent study by Interrante et al. [2004] similarlyshowed that people were highly accurate in walking without vision up to 30 ft in a real room and in ahigh-fidelity virtual model of the room presented in an HMD system. Although these last two studiessuggest a more mixed view of distance perception in virtual environments, there is a general consensusthat distance perception is distorted to varying degrees in virtual environments as compared to distanceperception in real environments.

The conclusion that people underestimate distances in virtual environments relative to the realenvironment may be premature, however. To date, studies examining distance perception in virtualenvironments have all used HMD systems, which provide a limited field of view (FOV) in both thehorizontal and vertical directions. Recently, Wu et al. [2004] examined the influence of vertical andhorizontal field of view on distance perception in the real environment. Subjects were asked to judgethe distance to targets while looking through a slot that reduced either the vertical or the horizontalfield of view. They found that subjects underestimated distance when their horizontal field of view

ACM Transactions on Applied Perception, Vol. 2, No. 3, July 2005.

218 • J. M. Plumert et al.

was unrestricted, but their vertical field of view was restricted to 21 degrees or less. However, whentheir vertical field of view was unrestricted, but their horizontal field of view was restricted, subjectsperformed as well as under full-view conditions. They conjecture that people are better able to integratethe distance along the ground plane when the entire area between the subject and the target is visible.Recent work by Creem-Regehr et al. [2005] however, indicates that not being able to see the areaaround one’s feet (a typical feature of HMDs) does not impair people’s ability to perceive distance inthe natural environment. Likewise, Knapp and Loomis [2004] found that restricting people’s FOV tothe extent typically experienced in HMDs does not impair distance perception in the real environment.Thus, restricted vertical FOV by itself does not appear to account for distorted distance perception invirtual environments involving HMDs.

Very little is known about how people perceive distance in virtual environments using large-screenimmersive display systems (LSIDs), such as CAVES. Large-screen environments typically provide amuch larger FOV than HMDs, perhaps making it easier to perceive egocentric distance. In the first twoexperiments reported below, we examined distance estimates in a virtual environment using a LSIDsystem. We measured distance perception by asking people to estimate how long it would take to walkto targets at distances ranging between 20 and 120 ft in an open, natural environment [see also Decetyet al. 1989]. We chose this environment because we wanted to test distance perception in the type ofenvironment used in our other work on simulated road crossing [Plumert et al. 2004]. Participantsestimated how long it would take to walk to each target distance by starting a stopwatch when theyimagined starting to walk to the target and stopping the stopwatch when they imagined reaching thetarget (without ever looking at the stopwatch). Participants made time-to-walk estimates both in thereal environment (a large grassy lawn in front of a university building) and in our virtual environment(a simulated version of the real environment). Time-to-walk estimates in both environments werecompared to actual time-to-walk estimates derived from a baseline walking task. Participants eithermade estimates in the real environment first or in the virtual environment first. We also conducted athird experiment to compare distance estimates using our time-to-walk measure and a more traditionalblindfolded walking measure.

2. EXPERIMENT 1

2.1 Method

2.1.1 Participants. Twenty-four undergraduates participated for course credit. There were 13 fe-males and 11 males.

2.1.2 Apparatus and Materials. A handheld stopwatch was used to record participants’ time esti-mates and a tape measure was used to measure the target distances.

2.1.3 Experimental Settings.

2.1.3.1 Real Environment. The real environment was an open, grassy lawn in front of a universitybuilding (Figure 1). To keep the real and virtual environments as similar as possible, we made everyattempt to keep the testing area free of distractions and objects. For example, we asked people (andducks) sitting within about 30 ft of the testing area to move and picked up all sticks and litter from thetesting area.

2.1.3.2 Virtual Environment. The virtual environment was a scene depicting the setting that servedas the real environment (Figure 2). This scene was displayed on three 10 ft × 8 ft-high screens placedat right angles relative to one another, forming a three-walled room. The screens were positioned 18 in.above the floor of the room. A black skirt hung from the bottom of the screens to the floor. Participants



Figs. 1 and 2. Photographs of the real environment and virtual environment. (Note that the picture of the real environmentwas taken from a place near the eye point used in the virtual environment.)

stood midway between the two side screens and 8 ft from the front screen. Three Electrohome DLV1280 projectors were used to rear project high-resolution, textured graphics onto the screens (1280 ×1024 pixels on each screen), providing participants with 270◦ of nonstereoscopic immersive visualimagery. The room housing the virtual environment was dark except for the light from the projectors.The viewpoint of the scene was adjusted for each participant’s eye height. Participants viewed thescene binocularly. The experiment was run on an 8-processor SGI Onyx computer with Infinite RealityGraphics. The software foundation was the Hank simulator, a real-time ground-vehicle simulationsystem designed to support complex scenarios [Cremer et al. 1997; Willemsen et al. 2003]. The particularsetup described above was chosen to validate previous research examining children’s road-crossingjudgments using the Hank simulator [Plumert et al. 2004].

2.1.4 Design and Procedure. Participants were tested individually in this and in all subsequent ex-periments. We first obtained an estimate of each participant’s typical walking speed by timing how longit took each participant to walk between two points in an uncluttered hallway. The first experimenterpositioned participants at the starting line and instructed them to walk at their normal speed past afinish line near the end of the hallway. The second experimenter started a stopwatch when participantsbegan walking and stopped the stopwatch as participants crossed the finish line. The distance betweenthe start and finish lines was 53 ft, falling approximately midway in the range of distances participantsestimated in the test portion of the experiment.

Following the baseline-walking task, participants made estimates of how long it would take them towalk to targets in the real and virtual environment. One-half of the participants made estimates in thevirtual environment first and one-half made estimates in the real environment first. Participants whofirst made estimates in the real environment were taken outside to a place at one end of the lawn facingthe university building. The first experimenter informed participants that the second experimenterwould stand at different places on the lawn in front of them and that their task was to imagine walkingto the second experimenter. The first experimenter then handed a stopwatch to the participants andtold them that they should start the stopwatch when they imagined starting to walk and to stopthe stopwatch when they imagined reaching the second experimenter (without ever looking at thestopwatch). Participants were given an opportunity to practice starting and stopping the stopwatch tomake sure that they knew how to operate the stopwatch. Before the start of each trial, participantsturned around so that they could not see the second experimenter moving into position. Each distance



was measured using a large tape measure. When the second experimenter was in position, he or sheretracted the tape measure. Participants then turned around and faced the target and started thestopwatch when they were ready. Participants kept their eyes open in the real and virtual environmentwhile making their estimates. After participants stopped the stopwatch, the experimenter recorded thetime elapsed. Participants completed time-to-walk estimates for six randomly ordered distances (20,40, 60, 80, 100, and 120 ft).

Participants who first made estimates in the virtual environment were taken into the simulatorfacility and positioned in the middle of the display screens depicting the outdoor scene. The experimenterinformed participants that an image of a person would appear at different places on the lawn in frontof them and that their task was to imagine walking to the person. The experimenter then handed astopwatch to the participants and told them that they should start the stopwatch when they imaginedstarting to walk and to stop the stopwatch when they imagined reaching the person. Participants weregiven an opportunity to practice starting and stopping the stopwatch to make sure that they knew how tooperate the stopwatch. For each trial, participants faced away from the display while the experimenterpressed a key to make the person appear on the lawn. Participants were then asked to turn around, facethe target, and start the stopwatch when they were ready. After participants stopped the stopwatch,the experimenter recorded the time elapsed. Again, participants did not look at the stopwatch duringor after the imagined walking task. All participants completed time-to-walk estimates for six randomlyordered distances (20, 40, 60, 80, 100, and 120 ft).

2.1.5 Measures

2.1.5.1 Actual Time to Walk. We estimated the amount of time actually required to walk the sixdistances for each participant by dividing each actual distance (i.e., 20, 40, 60, 80, 100, and 120 ft)by the participant’s walking speed. (Each participant’s walking speed was determined by dividing thebaseline-walking distance by the baseline-walking time.)

2.1.5.2 Time-to-Walk Estimates in the Real Environment. Each participant had six time-to-walkestimates in the real environment, representing the time elapsed between starting and stopping thestopwatch for each distance.

2.1.5.3 Time-to-Walk Estimates in the Virtual Environment. Each participant also had six time-to-walk estimates in the virtual environment, representing the time elapsed between starting andstopping the stopwatch for each distance.

2.2 Results

The analyses below focus on two primary questions. First, how closely did time-to-walk estimatescorrespond in the real and virtual environments? Second, how closely did time estimates in the realand virtual environments correspond to actual times?

2.2.1 Time-to-Walk Estimates. As shown in Figures 3(a) and (b), average time-to-walk estimateswere very similar across the real and virtual environments. We compared time-to-walk estimates acrossthe two environments in an environment order (real environment first vs. virtual environment first) ×environment (real versus virtual) × distance (20, 40, 60, 80, 100, versus 120 ft) repeated-measuresANOVA with the first factor as a between-subjects variable and the second and third factors as within-subjects variables. As expected, there was a significant effect of distance, F (5, 110) = 118.54, p < 0.001.There was also a significant environment order × distance interaction, F (5, 110) = 8.84, p < 0.001.Simple effects tests revealed a significant effect of environment order at distances of 60 ft and beyond,F ′s(1, 22) > 4.31, p′s < 0.05. For these longer distances, overall time-to-walk estimates were smallerACM Transactions on Applied Perception, Vol. 2, No. 3, July 2005.


Figs. 3(a) and (b). Mean time-to-walk estimates in the real environment first and virtual environment first conditions.

when people made estimates in the virtual environment first than when they made estimates in thereal environment first. There was also a significant environment order × environment interaction,F (1, 22) = 9.70, p < 0.01. Simple effects tests showed a significant effect of environment for the virtualenvironment first condition, F (1, 11) = 8.37, p < 0.05, but not for the real environment first condition,F (1, 11) = 2.89, ns. In the virtual environment first condition, time-to-walk estimates were smaller inthe virtual environment (M = 7.20, SD = 4.52) than in the real environment (M = 8.36, SD = 5.52). Inthe real environment first condition, estimates did not differ significantly in the real (M = 10.98, SD =6.19) and virtual environments (M = 11.86, SD = 7.04). Thus, time-to-walk estimates were remarkablysimilar across the two environments at virtually all distances in the real environment first condition.

2.2.2 Comparison to Actual Time to Walk. How well did time-to-walk estimates in the real andvirtual environment correspond to actual time to walk? Real and virtual time-to-walk estimates ineach condition were compared to actual times in separate Estimate (estimated time-to-walk vs. actualtime-to-walk) × distance (20, 40, 60, 80, 100, versus 120 ft) repeated-measures ANOVAs.

2.2.2.1 Real Environment first. For time-to-walk estimates in the real environment, there weresignificant effects of distance, F (5, 55) = 407.95, p < 0.001, estimate, F (1, 11) = 14.86, p < .01, and asignificant distance × estimate interaction, F (5, 55) = 8.78, p < 0.001 ([see Figure 3(a)]). Simple effectstests revealed a significant effect of estimate at distances of 60 ft and beyond, F ′s(1, 11) > 10.68, p′s <

0.01, indicating that people significantly undershot times at the longer distances. For time-to-walkestimates in the virtual environment, there were significant effects of distance, F (5, 55) = 248.95, p <

0.001, estimate, F (1, 11) = 5.023, p < 0.05, and a significant distance × estimate interaction, F (5, 55) =5.55, p < 0.001 [see Figure 3(a)]. Simple effects tests revealed a significant effect of estimate at distancesof 60, 100, and 120 ft, F ′s(1, 11) > 5.81, p′s < 0.05, indicating that people significantly undershot timesat longer distances.

2.2.2.2 Virtual Environment first. For time-to-walk estimates in the virtual environment, therewere significant effects of distance, F (5, 55) = 265.77, p < 0.001, estimate, F (1, 11) = 47.42, p < .001,and a significant distance × estimate interaction, F (5, 55) = 76.85, p < 0.001 [Figure 3(b)]. Simpleeffects tests revealed a significant effect of estimate at distances of 40 ft and beyond, F ′s(1, 11) >

9.56, p′s < 0.05. For time-to-walk estimates in the real environment, there were significant effectsof distance, F (5, 55) = 248.50, p < 0.001, estimate, F (1, 11) = 21.24, p < 0.001, and a significant



distance × estimate interaction, F (5, 55) = 30.67, p < 0.001 [Figure 3(b)]. Simple effects tests revealeda significant effect of estimate at distances of 40 ft and beyond, F ′s(1, 11) > 6.14, p′s < 0.05. Thus,people undershot times in both the real and virtual environment at distances of 40 ft and beyond whenthey made estimates in the virtual environment first.

2.3 Summary

Several results are noteworthy. First, people’s time-to-walk estimates were remarkably similar acrossthe real and virtual environments, particularly when they made estimates in the real environment first.This finding is inconsistent with previous investigations of distance perception in virtual environmentsusing HMD systems [Loomis and Knapp 2003; Thompson et al. 2004; Willemsen and Gooch 2002].Second, people began to undershoot time to walk in the real environment at 60 ft and in the virtualenvironment at 40 ft. Finally, time-to-walk estimates for distances of 60 ft and beyond were moredistorted in both environments when people experienced the virtual environment first. This finding isconsistent with the results of Witmer and Sadowski [1998].

Why did people undershoot time-to-walk estimates at the longer distances? One possibility is thatviewing the target while making a time-to-walk estimate interfered with spatial updating. In studies ofblindfolded walking, people presumably update their view of the environment as they locomote towardthe target [Rieser et al. 1990]. In imagined movement, we assume that people also update their view ofthe environment as they imagine moving toward the target. In situations where people imagine movingtoward a constantly viewed target, there may be a conflict between a spatially updated view of theenvironment generated through imagined movement and the concurrent nonupdated view specifiedby staying in the same place. This suggests that people may be better at imagining moving to thetarget in our task if they make their time-to-walk estimates without vision. We tested this hypothesisin Experiment 2 by having 10- and 12-year-old children and adults make time-to-walk estimates inreal and virtual environments with and without vision. We included 10- and 12-year-old children inthis experiment because large-screen immersive displays have been used in recent work on children’sroad-crossing behavior [Plumert et al. 2004]. Therefore, we wanted to determine whether children’stime-to-walk estimates were also similar in real and virtual environments.

3. EXPERIMENT 2

3.1 Method

3.1.1 Participants. Forty-eight 10- and 12-year-old children and adults participated. There were9 males and 7 females in the 10-year-old group, 11 males and 5 females in the 12-year-old group, and7 males and 9 females in the adult group. Children were recruited from a child research participantdatabase maintained by the Department of Psychology at the University of Iowa. Adults participatedto fulfill research credit for an introductory psychology course.

3.1.2 Apparatus and Materials. A handheld stopwatch was used to record participants’ time esti-mates and a lidar gun was used to measure the target distances. Sunglasses with the lenses, sides,and nose area blocked out were used to prevent participants from viewing targets while making timeestimates without vision.

3.1.3 Experimental Settings. The real and virtual environments were identical to those used inExperiment 1.

3.1.4 Design and Procedure. We first obtained an estimate of participants’ walking speeds usingthe same procedure as in Experiment 1, except that participants did the baseline-walking task twiceto obtain a more stable estimate of walking speeds (especially for children).ACM Transactions on Applied Perception, Vol. 2, No. 3, July 2005.


Fig. 4. Ten-year-olds’ mean time-to-walk estimates in the real and virtual environment.

Following the baseline-walking task, participants made estimates of how long it would take them towalk to targets at six distances (i.e., 20, 40, 60, 80, 100, and 120 ft) in the real and virtual environment.Estimates of time to walk for each environment were made both with and without vision, resultingin four sets of judgments for each participant. The procedure for making time-to-walk estimates wasthe same as that used in the first experiment, with the exception of the blindfolded trials. For thesetrials, participants viewed the target for approximately 4–5 s, put on the blindfold, and started thestopwatch when they were ready. We counterbalanced the order in which participants encounteredthe two environments. Thus, one-half of the performed the judgments in the virtual environment firstand one-half performed the judgments in the real environment first. Within each half, the order inwhich participants performed the blindfolded and sighted judgments was counterbalanced. The orderof distances was random.

3.1.5 Measures. As in Experiment 1, we estimated the amount of time actually required to walkthe six distances for each participant by dividing each actual distance by the participant’s averagewalking speed. Participants made six time-to-walk estimates in the real environment and in the virtualenvironment for both blindfolded and sighted trials, resulting in a total of 24 time-to-walk estimates.

3.2 Results

Again, we focus on two primary questions. First, how closely did time-to-walk estimates correspondin real and virtual environments? Second, how closely did time-to-walk estimates in real and virtualenvironments correspond to actual times? We addressed these questions separately for the three agegroups, because preliminary analyses suggested that the patterns of estimates varied across the threeage groups. We collapsed the data across the two environment orders and the two vision orders becausethe number of participants in each group was too small to conduct meaningful analyses on order.

3.2.1 Time-to-walk Estimates.

3.2.1.1 10-Year-Olds. As shown in Figure 4, 10-year-olds’ estimates in the virtual environmentwere generally smaller than in the real environment. We analyzed their time-to-walk estimates in



Fig. 5. Twelve-year-olds’ mean time-to-walk estimates in the real and virtual environment.

an environment (real versus virtual) × vision (sighted versus unsighted) × distance (20, 40, 60, 80,100, versus 120 ft) repeated-measures ANOVA. As would be expected, there was a significant effect ofdistance, F (5, 75) = 49.90, p < 0.001. There was also a significant effect of environment, F (1, 15) =10.87, p < 0.01, and a significant distance × environment interaction, F (5, 75) = 2.78, p < 0.05. Therewere no significant effects involving vision, however. Simple effects tests revealed a significant effect ofenvironment at distances of 40, 60, 80, and 120 ft, F ′s(1, 15) > 5.94, p < 0.05. The effect of environmentwas marginally significant at distances of 20 ft, F (1, 15) = 4.34, p = 0.054, and 100 ft, F (1, 15) = 3.99,p = 0.064. Thus, 10-year-olds tended to underestimate time-to-walk in the virtual environment relativeto the real environment at almost all distances (see Figure 4).

3.2.1.2 12-Year-Olds. Unlike the 10-year-olds, the 12-year-olds’ time-to-walk estimates were highlysimilar across the real and virtual environments (see Figure 5). We analyzed their time-to-walk esti-mates in an environment (real versus virtual) × vision (sighted versus unsighted) × distance (20, 40,60, 80, 100, versus 120 ft) repeated-measures ANOVA. As would be expected, this analysis yielded asignificant effect of distance, F (5, 75) = 30.16, p < 0.001. No other main effects or interactions were sig-nificant. Thus, 12-year-olds’ time-to-walk estimates were similar in the real and virtual environmentsat all distances and were not influenced by whether they made estimates while sighted or blindfolded.

3.2.1.3 Adults. Like the 12-year-olds, the adults’ time-to-walk estimates were very similar acrossthe real and virtual environments (see Figure 6). We analyzed their time-to-walk estimates in an en-vironment (real versus virtual) × vision (sighted versus unsighted) × distance (20, 40, 60, 80, 100,versus 120 ft) repeated-measures ANOVA. As expected, there was a significant effect of distance,F (5, 75) = 68.24, p < 0.001. There was no main effect of environment, F (1, 15) = 0.40, ns, but therewas a significant distance × environment interaction, F (5, 75) = 2.58, p < 0.05. Simple effects testsrevealed that adults’ time-to-walk estimates were smaller in the virtual than in the real environmentat 100 ft, F (1, 15) = 6.76, p < 0.05. Estimates in the real and virtual environment did not differ signif-icantly at any other distance, F ′s(1, 15) < 1.29, p′s > 0.20. Thus, adults’ time-to-walk estimates werehighly similar across the real and virtual environments and did not differ significantly in the sightedand unsighted conditions (see Figure 6).ACM Transactions on Applied Perception, Vol. 2, No. 3, July 2005.


Fig. 6. Adults’ mean time-to-walk estimates in the real and virtual environment.

3.2.2 Comparison to Actual Time to Walk. We also compared time-to-walk estimates to actual timesto examine the accuracy of people’s time-to-walk estimates. Sighted and blindfolded time-to-walk es-timates and actual times were entered into separate estimate (time-to-walk estimate versus actualtime) × distance (20, 40, 60, 80, 100, versus 120 ft) repeated-measures ANOVAs for each environment.Again, age groups were analyzed separately.

3.2.2.1 10-Year-Olds. For blindfolded and sighted estimates made in the real environment, therewere significant effects of distance, F ′s(5, 75) > 184.29, p′s < 0.001, estimate, F ′s(1, 15) > 18.84,p′s < 0.001, and a significant distance × estimate interaction, F ′s(5, 75) > 21.01, p′s < 0.001. Sim-ple effects tests of the distance × estimate interaction for sighted estimates revealed a significanteffect of estimate at distances of 60 ft and beyond, F ′s(1, 15) > 5.13, p′s < 0.05. Simple effects testsof the distance × estimate interaction for blindfolded estimates revealed a significant effect of esti-mate at distances of 40 ft and beyond, F ′s(1, 15) > 6.68, p′s < 0.05. For blindfolded and sighted esti-mates made in the virtual environment, there were significant effects of distance, F ′s(5, 75) > 185.04,p′s < 0.001, estimate, F ′s(1, 15) > 111.46, p′s < 0.001, and a significant distance × estimate in-teraction, F ′s(5, 75) > 31.88, p′s < 0.001. Simple effects tests of the distance × estimate interac-tion for sighted estimates revealed a significant effect of estimate at distances of 40 ft and beyond,F ′s(1, 15) > 24.53, p′s < 0.001. Simple effects tests of the distance × estimate interaction for blind-folded estimates revealed a significant effect of estimate for all distances, F ’s (1, 15) > 9.70, p’s < 0.01.Thus, 10-year-olds underestimated times in the real environment for distances of 40 to 60 ft and be-yond, and underestimated times in the virtual environment at distances of 20 to 40 ft and beyond (seeFigure 4).

3.2.2.2 12-Year-Olds. For blindfolded and sighted estimates made in the real environment, therewas a significant effect of distance, F ′s(5, 75) > 64.42, p′s < 0.001. Unlike the younger children, therewas no effect of estimate, F ′s(1, 15) < 1.22, ns, and no distance × estimate interaction, F ′s(5, 75) < 0.89,ns. For blindfolded and sighted estimates made in the virtual environment, there was a significant



effect of distance, F ′s(5, 75) > 120.62, p′s < 0.001, and a significant distance × estimate interaction,F ′s(5, 75) > 5.02, p′s < 0.001. Simple effects tests of the distance × estimate interaction for sightedestimates revealed a significant effect of estimate at distances of 80 and 100 ft, F ′s(1, 15) > 6.79,p′s < 0.05. Simple effects tests of the distance × estimate interaction for blindfolded estimates revealeda significant effect of estimate for 120 ft, F (1, 15) = 10.07, p < 0.01. Thus, 12-year-olds’ time-to-walkestimates were generally quite accurate except for the longest distances (see Figure 5).

3.2.2.3 Adults. For blindfolded and sighted estimates made in the real environment, there weresignificant effects of distance, F ′s(5, 75) > 223.90, p′s < 0.001, and estimate, F ′s(1, 15) > 6.18, p′s <

0.05, and a significant distance × estimate interaction, F ′s(5, 75) > 5.31, p′s < 0.001. Simple effectstests of the distance × estimate interaction for sighted estimates revealed a significant effect of estimateat distances of 60 ft and beyond, F ′s(1, 15) > 5.24, p′s < 0.05. Simple effects tests of the distance ×estimate interaction for blindfolded estimates revealed a significant effect of estimate at distances of80 ft and beyond, F ′s(1, 15) > 6.50, p′s < 0.05. For blindfolded and sighted estimates made in the virtualenvironment, there were significant effects of distance, F ′s(5, 75) > 166.73, p′s < 0.001, estimate,F ′s(1, 15) > 5.24, p′s < 0.05, and a significant distance × estimate interaction, F ′s(5, 75) > 6.47,p′s < 0.001. Simple effects tests of the distance × estimate interaction for sighted estimates revealeda significant effect of estimate at distances of 40 ft and beyond, F ′s(1, 15) > 5.57, p′s < 0.05. Simpleeffects tests of the distance × estimate interaction for blindfolded estimates revealed a significanteffect of estimate for distances of 80 ft and beyond, F ′s(1, 15) > 4.70, p′s < 0.05. Thus, adults began tosignificantly undershoot time-to-walk estimates at 40 ft when sighted and at 60 ft. when blindfolded(see Figure 6).

3.3 Summary

People’s sighted and unsighted time-to-walk estimates were very similar, suggesting that people canmentally update their movement to a target when either blindfolded or sighted. As in Experiment1, adults’ time-to-walk estimates in the real and virtual environment were very similar. This alsoheld true for 12-year-old children, although 10-year-olds underestimated times more in the virtualenvironment than in the real environment. At this point, it is unclear why 10-year-olds exhibited moreunderestimation in the virtual than in the real environment compared to 12-year-olds and adults. Otherstudies have shown that children’s ability to engage in imagined movement undergoes developmentalchange between the ages of 8 and 10 years [Gauvain and Rogoff 1989]. Possibly, the unfamiliarity of thevirtual environment disrupted 10-year-olds’ somewhat fragile ability to imagine walking to a target.Additional research is needed to determine exactly why 10- and 12-year-olds responded differently tothe virtual environment.

One possible reason for the discrepancy between our findings and those of others concerns differ-ences in the measures used to assess people’s distance perception. Previous studies comparing distanceperception in real and virtual environments have used visually directed action tasks such as triangu-lation [Loomis and Knapp 2003] or blindfolded walking [Witmer and Sadowski 1998]. Although ourtime-to-walk measure is similar in the sense that people are judging distance to fixed targets, it isdifferent in that it involves imagined rather than real movement toward those targets. People’s time-to-walk estimates may look highly similar in real and virtual environments because the processesinvolved in imagined movement remain constant across environments. This raises the question ofwhether the same processes are involved in imagined and blindfolded walking. As a first step in ad-dressing this question, we conducted a third experiment in which we directly compared time-to-walkestimates made with or without vision and walking without vision for a set of distances between 20 and70 ft.ACM Transactions on Applied Perception, Vol. 2, No. 3, July 2005.


4. EXPERIMENT 3

4.1 Method

4.1.1 Participants. Twenty-four undergraduates participated for course credit. There were 8 fe-males and 16 males.

4.1.2 Apparatus and Materials. A handheld stopwatch was used to record participants’ time esti-mates and a tape measure was used to measure the target distances and distances walked. Sunglasseswith the lenses, sides, and nose area blocked out were used to prevent participants from viewing targetswhile walking without vision and while making blindfolded time estimates.

4.1.3 Experimental Setting. The experimental setting was the real environment used in Experi-ments 1 and 2.

4.1.4 Design and Procedure. We first obtained an estimate of each participant’s walking speed usingthe same procedure as in Experiment 2.

Following the baseline-walking task, participants were taken outside to the far end of the lawn. Theyfirst made time-to-walk estimates for targets at six distances (i.e., 20, 30, 40, 50, 60, and 70 ft). (We chosea shorter set of distances to avoid having participants walk without vision for long distances.) One-halfof the participants made time-to-walk estimates while blindfolded and one-half made estimates whilesighted. The same procedure was used for the sighted and blindfolded time-to-walk estimates as inExperiment 2. After completing the time-to-walk estimates, all participants attempted to walk thesix target distances without vision. We first gave them practice with walking blindfolded in an areaperpendicular to the test area. Participants first faced away while the second experimenter stood at adistance of 25 ft. Participants then turned to face the second experimenter for approximately 4–5 s. Thefirst experimenter then instructed them to put on the blindfold, close their eyes, and walk to the target.Participants were also instructed to keep their eyes closed after they stopped walking so that theywould not receive any feedback about how close they came to the target. Participants continued to keeptheir eyes closed while the first experimenter led them back to the starting point. After completing fourpractice trials, participants completed six test trials at 20, 30, 40, 50, 60, and 70 ft. The procedure was thesame as that used for the practice trials except that we measured how far they walked prior to leadingthem back to the starting position. Again, they walked without vision back to the starting position. Theorder of distances was random for both the time-to-walk estimates and the blindfolded walking trials.

4.1.5 Measures. As in the previous experiments, we estimated the amount of time actually requiredto walk the six distances for each participant by dividing each actual distance by the participant’saverage walking speed. In order to directly compare time-to-walk estimates and blindfolded walking,we transformed the six time-to-walk estimates into distances by multiplying each time estimate by theparticipant’s baseline-walking speed.

4.2 Results

4.2.1 Time-to-Walk Estimates. The first question we addressed was whether time-to-walk estimatesdiffered across the blindfolded and sighted conditions [see Figures 7(a) and 7(b)]. Time-to-walk esti-mates were entered into a condition (blindfolded versus sighted time-to-walk estimates first) × distance(20, 30, 40, 50, 60, versus 70 ft) repeated-measures ANOVA, with the first factor as a between-subjectsfactor and the second as a within-subjects factor. As expected, there was a significant effect of distance,F (6, 132) = 71.11, p < 0.001. As in Experiment 2, there was no effect of condition, suggesting thattime-to-walk estimates did not differ depending on whether they were made with or without vision.We also compared time-to-walk estimates done with or without vision to the actual times in separate



Figs. 7(a) and (b). Blindfolded and sighted time-to-walk estimates.

estimate (time-to-walk versus actual time) × distance (20, 30, 40, 50, 60, versus 70 ft) repeated-measuresANOVAs [see Figures 7(a) and 7(b)]. For participants who made time-to-walk estimates while blind-folded, there was a main effect of distance, F (5, 55) = 114.59, p < 0.001, but no effect of estimate,F (1, 11) = 0.28, ns. Likewise, for participants who made time-to-walk estimates while sighted, therewas a main effect of distance, F (5, 55) = 178.21, p < 0.001, but no effect of estimate, F (1, 11) = 1.48,ns. Thus, time-to-walk estimates did not differ significantly from actual times in either condition acrossdistances of 20 to 70 ft.

4.2.2 Blindfolded Walking. As shown in Figure 8, the distances people walked without vision werevery close to the actual distances regardless of whether people made sighted or blindfolded time-to-walk estimates first. We compared distances walked with the target distances to determine whetherpeople significantly undershot (or overshot) the target distances (see Figure 8). One-sample t-testsrevealed that people who made sighted time-to-walk estimates before doing the blindfolded-walkingtask significantly undershot target distances at 20, 40, 60, and 70 ft, t ’s (11) < −2.21, p′s < 0.05. Peoplewho made blindfolded time-to-walk estimates before doing the blindfolded-walking task significantlyundershot target distances at 30, 40, and 50 ft., t ′s(11) < −2.28, p′s < 0.05. Thus, although the distanceswalked were very close to the target distances, there was some tendency to undershoot distance duringblindfolded walking.

4.2.3 Comparison of Time-to-Walk Estimates and Blindfolded Walking. The primary question ofinterest was how well people’s time-to-walk estimates corresponded to their walking without vision. Wecompared time-to-walk estimates (transformed into distances) and blindfolded walking in a condition(blindfolded versus sighted time-to-walk estimates first) × estimate (time-to-walk versus blindfoldedwalking) × distance (20, 30, 40, 50, 60, versus 70 ft) repeated-measures ANOVA with the first factor asa between-subjects factor and the second and third as a within-subjects factors. This analysis yieldedonly a significant effect of distance, F (5, 110) = 198.15, p < 0.001. There was no significant effect ofestimate, F (1, 22) = 1.65, p > 0.20, or interactions with estimate, suggesting that people’s time-to-walkestimates and walking without vision were highly similar (see Figure 9).ACM Transactions on Applied Perception, Vol. 2, No. 3, July 2005.


Fig. 8. Mean distance walked without vision.

Fig. 9. Mean distance walked in blindfolded walking task and timing task (transformed into distance).

4.3 Summary

The results of this experiment show that people’s time-to-walk estimates and their blindfolded walkingin the real environment were virtually identical [see also Decety et al. 1989]. In addition, both time-to-walk estimates and blindfolded walking were very accurate across distances of 20 to 70 ft. Thesimilarity between time-to-walk estimates and blindfolded walking is important because it suggests



that similar spatial updating mechanisms may underlie both imagined and blindfolded walking. Thisissue is considered in more detail below.

5. GENERAL DISCUSSION

A consistent pattern of results emerges from our experiments. Time-to-walk estimates were highlysimilar in virtual and real environments, particularly for older children and adults. In general, peoplebegan to undershoot sighted time-to-walk estimates at 40 ft and blindfolded time-to-walk estimatesat 60 ft. These findings are consistent with previous research showing that people are very good atwalking without vision to targets up to about 20 m. Our final experiment comparing imagined walkingwith blindfolded walking showed that people’s time-to-walk estimates (converted to distances) werealmost identical to the distances walked without vision [see also Decety et al. 1989]. Together, theseresults suggest that distance perception in virtual environments with large-screen immersive displaysis similar to distance perception in real environments.

The finding that time-to-walk estimates were very similar in real and virtual environments is surpris-ing given that several other studies have found that people typically underestimate distance in virtualenvironments that use HMDs. What accounts for this discrepancy? At least three possibilities come tomind. First, people may find it easier to perceive egocentric distance in virtual environments involvingLSIDs than in virtual environments involving HMDs. Unlike large-screen immersive displays, HMDshave restricted vertical and horizontal FOV. The importance of FOV in estimating distance in boththe real environment and in virtual environments has been the subject of recent controversy, however.According to Wu et al. [2004] a restricted vertical FOV (21◦ or less) leads to underestimation of distancein the real environment. Likewise, Witmer and Sadowski [1998] suggest that the reduced vertical FOVin HMDs may degrade convergent linear perspective and relative size as cues to distance. In contrast,Knapp and Loomis [2004] recently found that a reduced vertical FOV similar to that experienced withHMDs had no impact on blindfolded walking in the real environment. For distances between 2 and 15 m,blindfolded walking was highly accurate under conditions of unrestricted FOV (120◦ vertical) and re-duced FOV (43◦ vertical). Likewise, Creem-Rehehr et al. [2005] found that not being able to see thearea around one’s feet (a typical feature of HMDs) does not impair people’s ability to perceive distancein the real environment. Together, these findings suggest that the reduced vertical FOV in commonlyused HMDs does not account for underestimation of distance in virtual environments using HMDs.However, this does not rule out the possibility that reduced vertical FOV in combination with otheraspects of virtual environments contributes to underestimation of distance in HMDs. In addition, theremay be other differences between LSIDs and HMDs, including the weight of the HMD itself [Willemsenet al. 2004], that account for the discrepancy between our findings and those of other researchers. Asa first step in determining what those differences might be, research is needed that directly comparesdistance perception in LSIDs and HMDs using the same measures.

A second possible reason for the discrepancy between findings concerns differences in the tasks usedto assess people’s distance perception. Previous studies comparing distance perception in real andvirtual environments have used visually directed action tasks such as triangulated walking [Loomisand Knapp 2003] or blindfolded walking [Witmer and Sadowski 1998]. Our experiments comparingdistance perception in real and virtual environments, relied on imagined walking. People’s time-to-walkestimates may look highly similar in real and virtual environments, because the processes engaged inimagined movement remain constant across environments. Thus, there may be something particularto time-to-walk estimates that accounts for the similarity between real and virtual environments. Ourthird experiment, however, showed that people’s time-to-walk estimates (converted into distances) andthe distances walked without vision were virtually identical. Moreover, consistent with other studies ofvisually directed action in the real environment, time-to-walk estimates and blindfolded walking were



highly accurate up to around 70 ft, suggesting that the same spatial updating processes are used inimagined and blindfolded walking.

A third source of variation that may partially account for the difference between our results andthose of previous studies is the structure and familiarity of the environment and the targets. In ourexperiments, subjects judged the distance to a standing person (a real person in the real environmentand an image of a person in the virtual environment) in a familiar outdoor environment with a richtexture gradient. Most of the experiments that report substantial distance compression in virtual envi-ronments have used more confined, indoor environments such as hallways. Moreover, the targets weredisks or balls on the floor. The familiar size of our targets, the expansiveness of our environment, andthe subjects’ everyday experience in the real environment may have provided better cues to depth,leading to improved distance estimation in our virtual environment.

What about the claim that imagined walking and blindfolded walking share common underlyingmechanisms? Visually directed actions such as blindfolded walking or triangulated walking are thoughtto engage a spatial updating system [Rieser et al. 1990, 1986; Rieser and Rider 1991]. According to Rieserand his colleagues, this spatial updating system is based on the learned correlation between optic flowinformation and efferent/proprioceptive information generated through everyday walking with vision.From this perspective, access to the spatial updating system depends critically on actual movement.This suggests that people should be good at estimating location during blindfolded locomotion butnot during imagined locomotion. Support for this argument comes from numerous studies showingthat shifts of spatial perspective are easy if people are allowed to physically rotate to the new spatialperspective, but hard if people have to imagine rotating to the new spatial perspective [e.g., Loomis et al.1999a; Rieser et al. 1986, 1994; Wang and Spelke 2000; Wraga et al. 2000]. Importantly, recent work hasshown that people have much more difficulty with imagined updating of spatial perspective after self-rotations than after self-translations [May 2004]. May concludes that people’s difficulty with imaginedself-rotation is the result of a conflict between one’s current perspective (represented by sensorimotorobject-location codes) and the imagined perspective (represented by cognitive codes of the same objectlocations). In the case of imagined self-translation, there is less conflict between sensorimotor andcognitive codes because the current and imagined heading are the same, leading to shorter responselatencies and smaller pointing errors after a change in imagined perspectives. Our finding that imaginedwalking and blindfolded walking resulted in nearly identical distance judgments also supports the ideathat people have little trouble updating imagined self-translations. Together, these findings leave openthe possibility that imagined and blindfolded walking share the same spatial updating mechanisms.Further work is needed to examine the cognitive processes (including timing mechanisms) involved inspatial updating without vision.

We end with a note of caution about the conclusion that distance perception in large-screen immersivevirtual environments is similar to distance perception in the real world. In particular, the results of Ex-periment 1 showed clear order effects on distance perception [see also Witmer and Sadowski 1998]. Thatis, performing virtual environment time-to-walk estimates first impacted subsequent real environmenttime-to-walk estimates (i.e., leading to greater underestimation) and performing real environment time-to-walk estimates first influenced subsequent virtual environment time-to-walk estimates (i.e., leadingto less underestimation). One interpretation of these results is that people calibrated their time-to-walkestimates to the environment that they experienced first. Although it is not terribly surprising thatexperiencing the real environment first grounded people’s judgments in the virtual environment, it issurprising that the brief experience in the virtual environment impacted people’s judgments in the realenvironment. Interestingly, recent work in other labs has also shown that people very quickly recali-brate their locomotion in the real environment based on changes in the relation between optic flow andproprioceptive information experienced in a large-screen immersive virtual environment [Mohler et al.



2004]. The persistence of the recalibration after subjects are reexposed to the natural coupling betweenvisual motion and action in the real world is unknown. However, these kinds of recalibration effectssuggest that some caution is warranted in drawing conclusions about real-world and virtual-world dis-tance perception until more is known about how experiences in virtual environments affect perceptionin the real environment and vice versa.

6. DIRECTIONS FOR FUTURE WORK

At least three issues should be addressed in future work. The first is the order effects discussed above.Experiment 1 should be replicated using a full factorial design in which the two environments and thetwo environment orders are fully crossed (i.e., include conditions in which participants make judgmentsin the virtual environment first and second or make judgments in the real environment first and second).A fully crossed design would shed light on whether there is something special about moving from oneenvironment to the other or about doing a second set of judgments (regardless of the environmentpreviously experienced). The second issue that deserves further attention is a direct comparison ofdistance perception in HMDS and LSDS using the same environment and the same measures. Such acomparison would more clearly answer the question of whether distance perception is better in LSDSthan in HMDS. A third issue that deserves further consideration is whether distance perception isbetter in virtual environments depicting large-scale, outdoor environments than in those depictingsmall-scale, indoor environments (e.g., hallways). As noted above, our environment contained a varietyof rich texture and relative size cues. A comparison of the same absolute distances in indoor versusoutdoor environments would tell us whether people find it easier to judge distance in some types ofenvironments than in others. Answers to all of these questions are needed before definitive conclusionscan be drawn about distance perception in virtual environments using large-screen display systems.

ACKNOWLEDGMENTS

This research was supported by grants from the National Science Foundation (IIS 00-02535 and EIA-0130864) and the National Center for Injury Prevention and Control through the University of IowaInjury Prevention Research Center (R49/CCR721682).

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